Search

With the rapid development of the Internet era and the high popularity of computers, China's economy, politics, and culture have made rapid progress in recent years. In order to adapt to this era of rapid change, China's higher education has also undergone great changes and carried out reform and innovation. As the top priority in the Internet era, computers should be reformed first. The traditional education model is mainly teacher-centered and classroom-centered. The disadvantages of this teaching mode are that students' subjective initiative cannot be fully developed and brought into play. Traditional teaching pays attention to knowledge teaching and ignores ability training. It emphasizes speaking and neglects practice. Therefore, in order to meet the development needs of the new era and cultivate modern talents, colleges and universities began to explore the innovation and entrepreneurship education mode. There is an application of TRIZ theory to the cultivation of innovation and entrepreneurship education, and TRIZ theory is a theory for solving invention problems. Its main content is to find its own laws in the process of technological evolution and analyze the laws to get a general solution, but there are many obstacles in exploring the mode of innovation and entrepreneurship education; for example, many governments do not support this education reform and do not broadcast relevant education and teaching funds for schools, resulting in uneven configuration of software and hardware in schools. According to the survey, the proportion of innovation and entrepreneurship education in the eastern coastal areas is as high as 70%, while the proportion of innovation and entrepreneurship education in the central and western regions is only 10%. This set of data shows that regional differences are the main reason for the differences in education development. In terms of the proportion of education funds, although the annual education funds will gradually increase in recent years, the proportion is still a drop in the bucket in front of huge fiscal revenue, with the highest being only 0.1%. Therefore, in order to improve the current situation of innovation and entrepreneurship education mode in colleges and universities, according to their own characteristics, the reform is carried out from the aspects of practical ability, competition participation, and training plan, and the BP neural network model is introduced to predict and analyze the effect of teaching methods. The results of this paper are investigated and analyzed. Finally, the investigation on the development of KAB course and SYB course and the analysis on the stability and error of BP neural network are within the allowable range, this shows that the model established in this paper is more favored by students than the traditional teaching methods, more in line with the social reality, the effect is more ideal, and the result is more stable. [ABSTRACT FROM AUTHOR]

In this paper, the periodicity of a class of nonautonomous fuzzy neural networks with impulses, reaction-diffusion terms, and distributed time delays are investigated. By establishing an integro-differential inequality with impulsive initial conditions and time-varying coefficients, employing the M -matrix theory, Poincar mappings, and fixed point theory, several new sufficient conditions to ensure the periodicity and global exponential stability of the formulated system are obtained. It is worthwhile to mention that our technical methods are practical, in the sense that all new stability conditions are stated in simple algebraic forms, and an optimization method is provided to estimate the exponential convergence rate, so their verification and applications are straightforward and convenient. The validity and generality of our methods are illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

The topic of this paper is to study the numerical solution of the fourth-order partial differential equation and analyze its visual application in software simulation. Therefore, for the initial circular domain, the expansion should be transformed into a fourth-order problem in the plane dimension. Then, we introduced appropriate-weighted Sobolev space based on the improved neural network algorithm and established a weak form and the corresponding discrete form for each one-dimensional fourth-order problem. The approximation properties of the cubic Hermite interpolation operator are used to verify the error value of the approximation solution. After obtaining the relevant algorithms, numerical empirical analysis is carried out to prove that the proposed algorithm is effective. Therefore, this article applies it to the visualization simulation technology, and the visualization module mainly completes two tasks: collecting geometric data and drawing models. At present, the application of the visualization module in the program mainly has two aspects: on the one hand, the boundary information of geometry can be obtained by screening the existing database so that the boundary model can be displayed in the visualization; on the other hand, from the calculation result file. Read the geometric information of particles and boundaries and perform dynamic simulation playback of the calculated results. [ABSTRACT FROM AUTHOR]

Mass sports has become a world trend, setting off a new health revolution in the world. Mass fitness programs not only enrich people's lives. It not only relieves the psychological pressure of modern people but also promotes people's health and improves people's quality of life. According to the time-consuming stability of neural network algorithm, this paper proposes a sports video recognition algorithm based on BP neural network. The static and dynamic features are classified by BP neural network, and the basic probability assignment is constructed according to the preliminary recognition results. At the same time, we use evidence theory to fuse the preliminary results and get the results of motion video recognition. It can be applied to the generation model of the feasible scheme of mass sports fitness. Relevant experiments show that the whole model that generates the feasible mass sports fitness scheme can accurately generate the sports fitness scheme of multiple patient users and ensure the rationality and safety of the sports fitness scheme. [ABSTRACT FROM AUTHOR]

The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors' teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor's weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers' teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers' teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation's dependability and makes the outcomes more effective and objective. [ABSTRACT FROM AUTHOR]

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes. [ABSTRACT FROM AUTHOR]

In order to identify the uncertain static and dynamic fragile nodes in new energy, the instability and randomness of new energy bring new challenges to the identification of vulnerable nodes in a power grid. Due to the characteristics of low cost and low energy consumption of new energy, people have paid much attention to the exploration and development of new energy. Due to the uncertainty of new energy, it is needed to properly analyze the uncertainty factors. To analyze the uncertainty factors in new energy using the framework of power big data artificial intelligence analysis based on cost-benefit analysis (CBA), it is required to carry out Fourier transform and extract the data characteristic matrix so that a vulnerability risk prediction index can be obtained by using a fuzzy convolution algorithm and binarization, and the safety form between the uncertainty factors in new energy and power stations can be evaluated. In this paper, a fuzzy neural network algorithm is proposed to identify the static and dynamic fragile nodes based on the uncertainty in new energy, so as to ensure the security and stability of the power generation system. The safety performance of the power station system is detected through different levels of early warning sensitivity. The simulation model of the above algorithm is constructed in MATLAB. The simulation results show that the proposed algorithm increases the sensitivity of the early warning system of the power station and the sensitivity of triggering the early warning system and improves the security of the power station system as a whole. [ABSTRACT FROM AUTHOR]

Many encryption systems face two problems: the key has nothing to do with the plaintext; only a single chaotic sequence is adopted during the encryption. To solve the problems, this paper proposes an image encryption method based on Hopfield neural network and bidirectional flipping. Firstly, the plaintext image was segmented into blocks, the resulting image matrix was block scrambled, and each block was bidirectionally flipped to complete the scrambling process. After that, the plaintext image was processed by the hash algorithm to obtain the initial values and control parameters of the chaotic system, producing a pseudo-random sequence. Then, a diffusion matrix was generated through the optimization by Hopfield neural network and used to derive a ciphertext image through diffusion transformation. Experimental results show that our algorithm is highly sensitive to plaintext, strongly resistant to common attacks, and very efficient in encryption. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of exponential stability for the Hopfield neural network with time-varying delays. Different from the existing results, we establish new stability criteria by employing the method of variation of constants and Gronwall's integral inequality. Finally, we give several examples to show the effectiveness and applicability of the obtained criterion. [ABSTRACT FROM AUTHOR]

This study consolidates input-output data from 42 sectors across 31 provinces and regions in China into a unified dataset for 42 industrial sectors within eight major economic zones. Leveraging the maximum entropy method, we identify significant interindustrial relationships, subsequently forming a directed, weighted, complex network of these ties. Building upon this intricate network, we analyze its foundational statistical attributes. The stability of the network's structure is further assessed through simulations of varied network attacks. Our findings demonstrate that the maximum entropy method is adept at extracting notable relationships between industrial sectors, facilitating the creation of a cogent complex interindustrial network. Although this established network exhibits high stability, it calls for targeted policy interventions and risk management, especially for industries with pronounced degree centrality and betweenness centrality. These pivotal industry nodes play a decisive role in the overall stability of the network. The insights derived from our examination of complex interindustrial networks illuminate the structure and function of industrial networks, bearing profound implications for policymaking and propelling sustainable, balanced economic progress. [ABSTRACT FROM AUTHOR]

The global dynamic behavior of an age-structured hand-foot-mouth disease (HFMD) model with saturation incidence and time delay is investigated in the work. The time delay occurs during the transition from latent to infectious individuals. Firstly, the model is expressed as an abstract Cauchy problem. The presence of equilibria is then pointed; meanwhile, we define the model's basic reproduction number R 0 . The conclusions show that a threshold of R 0 = 1 can be used to evaluate whether the disease is on the verge of extinction or is still present. When R 0 < 1 , the disease-free equilibrium is globally stable. While R 0 > 1 , there exists an endemic equilibrium, and the global stability of the endemic equilibrium is also demonstrated. Finally, some numerical examples are given to demonstrate the obtained conclusions. [ABSTRACT FROM AUTHOR]

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM. [ABSTRACT FROM AUTHOR]

Since the 1980s, the research of artificial neural networks in the field of artificial intelligence has become more and more common. It accepts nonlinear parallel processing, has strong learning and flexibility, and can be used for influencing factor analysis. The ideal power values and triggers are obtained in the Hopfield network model using genetic algorithm, which best avoids the drawbacks of the Hopfield network model instillation learning method. Through the BP of mobile human-computer interaction equipment, hereditary, genetic algorithms, and Hi-PLS regression method in the artificial neural network, the psychological pressure of college students is identified, evaluated, and predicted from three dimensions such as learning, life, and personal events. This makes it possible to understand the current physical and mental conditions of the students in a timely manner, guide to relieve anxiety and fear, and reach a safe psychological level. The three test results are less than 1%, which has high research significance and value. [ABSTRACT FROM AUTHOR]

Today, the Internet of Things (IoT), often referred to as smart home technology, is used by many individuals in their daily life. The majority of IoT devices include a companion mobile application that consumers must install on their tablet or smartphone to operate, configure, and interface with the IoT device. Multiple kinds of IoT systems have grown in popularity alongside the growth of information engineering. The accessibility of a user interface (UI) in an IoT system is becoming increasingly crucial. This causes major issues with accuracy and evaluation time. This research suggests a discrete mathematical model for IoT-enabled interactive UI design impact assessment approach to address the concerns of low evaluation accuracy and long evaluation time in existing interactive UI design effect evaluation methods. It creates an IoT-enabled interactive UI design effect assessment index system using interactive UI design vision, hearing, and feeling. It gets IoT-enabled interactive UI design effect evaluation indexes, builds a judgment matrix, derives the weight coefficient of each evaluation index, and employs the hierarchy based on the weight coefficient produced. To complete the assessment of the IoT-enabled interactive UI design effect, the analytical approach evaluates the relative value of the seven evaluation indicators by utilizing the discrete Hopfield neural network to develop the evaluation mathematical model. The simulation results reveal that the suggested method's IoT-enabled interactive UI design effect evaluation accuracy is greater, while the evaluation time is shorter. [ABSTRACT FROM AUTHOR]

As a popular technology in the information age, blockchain is having a profound impact on various industries, including the field of education. Blockchain technology can accelerate the modernization of education in China, adapt to the new situation of epidemic prevention and control needs, and promote the renewal of the management and training modes of college students. Blockchain technology is decentralized, tamper-proof, traceable, trackable, open and transparent, which is conducive to the realization of individual student-centered teaching and learning, as well as an open, and transparent teaching process, which can motivate the learning motivation of English majors and improve the effectiveness of the student management and training models. To address the problem of low accuracy of the traditional English proficiency classification methods, this article aims to explore the application of blockchain technology in the management and training model of English majors and its feasibility. We propose a proficiency evaluation model based on a Discrete Hopfield Neural Network (DHNN). Firstly, the hierarchical analysis method is used to construct the evaluation index system of students' English ability, and then the ability classification indexes are divided into 5 levels.The network achieves the classification of students' English proficiency through the associative memory of the classification criteria, and the classification results are compared with those of the BPNN model. The simulation results show that the classification accuracy of the BPNN model is 80.0% and that of the DHNN model is 100.0%. The DHNN model has improved the classification accuracy and generalization ability, and the model establishment process is simple and the results are intuitive, which verifies the effectiveness of the proposed model [ABSTRACT FROM AUTHOR]

Hopfield neural network (HNN) is considered as an artificial model derived from the brain structures and it is an important model that admits an adequate performance in neurocomputing. In this article, we solve a dynamical model of 3D HNNs via Atangana–Baleanu (AB) fractional derivatives. To find the numerical solution of the considered dynamical model, the well-known Predictor-Corrector (PC) method is used. A number of cases are taken by using two different sets of values of the activation gradient of the neurons as well as six different initial conditions. The given results have been perfectly established using the different fractional-order values on the given derivative operator. The objective of this research is to investigate the dynamics of the proposed HNN model at various values of fractional orders. Nonlocal characteristic of the AB derivative contains the memory in the system which is the main motivation behind the proposal of this research. [ABSTRACT FROM AUTHOR]

The time delay caused by transmission in neurons is often ignored, but it is demonstrated by theories and practices that time delay is unavoidable. A new chaotic neuron model with time delay self-feedback is proposed based on Chen's chaotic neuron. The bifurcation diagram and Lyapunov exponential diagram are used to analyze the chaotic characteristics of neurons in the model when they receive the output signals at different times. The experimental results exhibit that it has a rich dynamic behavior. In addition, the randomness of chaotic series generated by chaotic neurons with time delay self-feedback under different conditions is verified. In order to investigate the application of this model in image encryption, an image encryption scheme is proposed. The security analysis of the simulation results shows that the encryption algorithm has an excellent anti-attack ability. Therefore, it is necessary and practical to study chaotic neurons with time delay self-feedback. [ABSTRACT FROM AUTHOR]

The article is devoted to the problem of optimization of search request ranking algorithms in the digital information retrieval system. The algorithm of functioning of the neural network ranking unit based on Hopfield neural network is built. The ability to generate a ranked list of pages found as a result of the request in the digital information retrieval system can be provided by solving two problems of integer optimization: the problem of assignment of combinatorial sets of criteria for assessing the relevance of web page search and the problem of sorting of numbers—relevance values. The architecture of the neural network model based on the dynamic Hopfield neural network with binary output function designed for combinatorial optimization of the final list of documents found in the digital information retrieval system was synthesized. Promising variants of neural network models with binary output function of neurons for synthesis of the optimal evaluation plan with a combinatorial set of criteria by solving the problem of assignment were built. It has been proven that the built models differ in the rules for determining the coefficients of synaptic connections and external shifts; each of the created rules can be used independently or in different combinations with one another. In the course of analytical research, it was found that the optimization formulation of the problem of sorting of relevance values of search pages is identical to the problem of assignment of combinatorial groups of evaluation criteria provided that the elements of the performance matrix of the latter are defined as linear combinations of relevance values. [ABSTRACT FROM AUTHOR]